A Subject-Independent Method for Automatically Grading Electromyographic Features During a Fatiguing Contraction

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis...

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Vydané v:IEEE transactions on biomedical engineering Ročník 59; číslo 6; s. 1749 - 1757
Hlavní autori: Chattopadhyay, Rita, Jesunathadas, Mark, Poston, Brach, Santello, Marco, Ye, Jieping, Panchanathan, Sethuraman
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.06.2012
Institute of Electrical and Electronics Engineers
Predmet:
Adult
Algorithms
Biological and medical sciences
Computer algorithm
Computerized, statistical medical data processing and models in biomedicine
Electrodiagnosis. Electric activity recording
electromyogram (EMG)
Electromyography
Electromyography - methods
Fatigue
Feature extraction
Fingers - physiology
Force
Humans
Indexes
Investigative techniques, diagnostic techniques (general aspects)
Isometric Contraction - physiology
Loading
Male
Medical management aid. Diagnosis aid
Medical sciences
Muscle Fatigue - physiology
Muscle, Skeletal - physiology
Muscles
Nervous system
Reproducibility of Results
Sensitivity and Specificity
Human
Electrodiagnosis
Computer algorithm
Electrophysiology
Electromyography
Method
electromyogram (EMG)
Algorithm
Contraction
Biomedical engineering
ISSN:0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2012.2193881